论文标题
一项关于ML4VI的调查：将机器学习进展用于数据可视化
A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization
论文作者
论文摘要
受到机器学习（ML）的巨大成功的启发，研究人员已将ML技术应用于可视化，以实现可视化的更好的设计，开发和评估。近年来，这种称为ML4VI的研究分支正在引起研究的越来越多。为了成功地适应ML技术来可视化，对ML4Visis的整合的结构化理解。在本文中，我们系统地调查了88项ML4VIS研究，目的是回答两个激励问题：“ ML可以帮助哪些可视化过程？”和“如何使用ML技术来解决可视化问题？”这项调查揭示了ML技术的使用可以使可视化效果受益的七个主要过程：数据处理4VI，数据VIS映射，InsightCommunication，样式模仿，VIS相互作用，VIS读取，VIS读取和用户分析。这七个过程与ML4VIS管道中现有的可视化理论模型有关，旨在阐明ML辅助可视化在一般可视化中的作用。同时，这七个过程被映射到ML中的主要学习任务，以使ML的功能与可视化的需求保持一致。在ML4VIS管道和ML-VIS映射的背景下，讨论了ML4VI的当前实践和未来机会。尽管在ML4VI领域仍然需要更多的研究，但我们希望本文可以为将来的探索提供垫脚石。该调查的基于Web的交互式浏览器可在https://ml4vis.github.io上获得。
Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VISis needed. In this paper, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: "what visualization processes can be assisted by ML?" and "how ML techniques can be used to solve visualization problems?" This survey reveals seven main processes where the employment of ML techniques can benefit visualizations:Data Processing4VIS, Data-VIS Mapping, InsightCommunication, Style Imitation, VIS Interaction, VIS Reading, and User Profiling. The seven processes are related to existing visualization theoretical models in an ML4VIS pipeline, aiming to illuminate the role of ML-assisted visualization in general visualizations.Meanwhile, the seven processes are mapped into main learning tasks in ML to align the capabilities of ML with the needs in visualization. Current practices and future opportunities of ML4VIS are discussed in the context of the ML4VIS pipeline and the ML-VIS mapping. While more studies are still needed in the area of ML4VIS, we hope this paper can provide a stepping-stone for future exploration. A web-based interactive browser of this survey is available at https://ml4vis.github.io
